CI Lectures Series - AI for Intelligent Scheduling Systems

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Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this lecture, Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques will described.


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  • Date: 12 May 2021
  • Time: 05:00 PM to 06:00 PM
  • All times are (UTC+01:00) Lisbon
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  Speakers

Ana Madureira Ana Madureira of ISEP - IPP

Biography:

Ana Madureira was born in Moçambique, in 1969. She got his BSc degree in Computer Engineering in 1993 from ISEP, master’s degree in electrical and Computers Engineering–Industrial Informatics, in 1996, from FEUP, and the PhD degree in Production and Systems, in 2003, from University of Minho, Portugal. She became IEEE Senior Member in 2010. She had been Chair of IEEE Portugal Section (2015-2017), Vice-chair of IEEE Portugal Section (2011-2014) and Chair/Vice-Chair of IEEE-CIS Portuguese chapter. She was Chair of University Department of IEEE R8 Educational Activities Sub-Committee (2017-2018). She was IEEE R8 Secretary (2019-2020). She is External Member Evaluation Committee of the Agency for Assessment and Accreditation of Higher Education - A3ES (Agência de Avaliação e Acreditação do Ensino Superior) for the scientific area of Informatics of Polytechnic Higher Education (2012- ). Currently she is Coordinator Professor at the School of Engineering–Polytechnic of Porto (ISEP/IPP) and Director of the Interdisciplinary Studies Research Center (ISRC). In the last few years, she was author of more than 100 scientific papers in scientific conference proceedings, journals and books. Her main areas of research are: artificial intelligence; metaheuristics; user modeling; dynamic scheduling; data science